Deep learning-empowered crop breeding: intelligent, efficient and promising

Xiaoding Wang; Haitao Zeng; Limei Lin; Yanze Huang; Hui Lin; Youxiong Que

doi:10.3389/fpls.2023.1260089

Deep learning-empowered crop breeding: intelligent, efficient and promising

Front Plant Sci. 2023 Oct 3:14:1260089. doi: 10.3389/fpls.2023.1260089. eCollection 2023.

Authors

Xiaoding Wang¹, Haitao Zeng¹, Limei Lin¹, Yanze Huang², Hui Lin¹, Youxiong Que^{3

4}

Affiliations

¹ Fujian Provincial Key Lab of Network Security & Cryptology, College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China.
² School of Computer Science and Mathematics, Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China.
³ Key Laboratory of Sugarcane Biology and Genetic Breeding, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University, Fuzhou, China.
⁴ National Key Laboratory for Tropical Crop Breeding, Institute of Tropical Bioscience and Biotechnology, Chinese Academy of Tropical Agricultural Sciences, Hainan, China.

Abstract

Crop breeding is one of the main approaches to increase crop yield and improve crop quality. However, the breeding process faces challenges such as complex data, difficulties in data acquisition, and low prediction accuracy, resulting in low breeding efficiency and long cycle. Deep learning-based crop breeding is a strategy that applies deep learning techniques to improve and optimize the breeding process, leading to accelerated crop improvement, enhanced breeding efficiency, and the development of higher-yielding, more adaptive, and disease-resistant varieties for agricultural production. This perspective briefly discusses the mechanisms, key applications, and impact of deep learning in crop breeding. We also highlight the current challenges associated with this topic and provide insights into its future application prospects.

Keywords: challenge; crop breeding; deep learning; prospect; smart breeding.

Grants and funding

The authors declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Key Research and Development Program of China (2022YFD2301100 and 2019YFD1000503), the Special Fund for Science and Technology Innovation of Fujian Agriculture and Forestry University (CXZX2020081A), and the Agriculture Research System of China (CARS-17).